A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level

Gasqui, Patrick; Trommenschlager, Jean-Marie

doi:10.1038/s41598-017-09322-x

Cited by 8 publications

(8 citation statements)

References 78 publications

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“…The residual variance σ 2 is 0.0168 kg. This result is similar to the model fit obtained with the physiology-based model of Gasqui and Trommenschlager (2017) for milk production on udder level on a milking session basis. When fitting an individual nonlinear Wood model for each QL, without taking any of the other covariates into account, we could only reach an MPE of 17.5%: ln(QMY) = β 0 + β 1 × POS + β 2 × PAR + β 3 × POS × PAR + β 9 × DIM + β 4 × PAR × DIM + β 5 × ln(DIM) + β 6 × POS × ln(DIM) + β 7 × MI' + β 8 × MI' × DIM + e.…”

Section: Data Model and Goodness-of-fitsupporting

confidence: 85%

“…An overview of the available lactation models are given in Dematawewa et al (2007); however, this approach becomes uninformative once the normal course of the curve is disrupted, and does not take variability caused by differing milking intervals (MI) into account, which is important in automated milking systems. Gasqui and Trommenschlager (2017) recently developed a physiology-based model to predict udder yield at milking session level, but this white-box model is rather complex and requires theoretical assumptions and further data-based testing. To our knowledge, no specific models have been proposed for short-term (1 to 50 d) Method for short-term prediction of milk yield at the quarter level to improve udder health monitoring predictions of milk yield at quarter level of individual cows based on real data.…”

Section: Introductionmentioning

confidence: 99%

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Method for short-term prediction of milk yield at the quarter level to improve udder health monitoring

Adriaens

Huybrechts

Aernouts

et al. 2018

Journal of Dairy Science

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Udder health problems are often associated with milk losses. These losses are different between quarters, as infected quarters are affected both by systemic and pathogen-specific local effects, whereas noninfected quarters are only subject to systemic effects. To gain insight in these losses and the milk yield dynamics during disease, it is essential to have a reliable reference for quarter-level milk yield in an unperturbed state, mimicking its potential yield. We developed a novel methodology to predict this quarter milk yield per milking session, using an historical data set of 504 lactations collected on a test farm by an automated milking system from DeLaval (Tumba, Sweden). Using a linear mixed model framework in which covariates associated with the linearized Wood model and the milking interval are included, we were able to describe quarter-level yield per milking session with a proportional error below 10%. Applying this model enables us to predict the milk yield of individual quarters 1 to 50 d ahead with a mean prediction error ranging between 8 and 20%, depending on the amount of historical data available to estimate the random effect covariates for the predicted lactation. The developed methodology was illustrated using 2 examples for which quarter-level milk losses are calculated during clinical mastitis. These showed that the quarter-level mixed model allows us to gain insight in quarter lactation dynamics and enables to calculate milk losses in different situations.

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Section: Data Model and Goodness-of-fitsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Method for short-term prediction of milk yield at the quarter level to improve udder health monitoring

Adriaens

Huybrechts

Aernouts

et al. 2018

Journal of Dairy Science

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“…Two methods of udder treatment before milking are most common in dairy farming [40,41]. To perform the first of them, prior preparation of the solution and clean warm water is required.…”

Section: Discussion Of the Results Obtained In Studying The Technical Means For Mechanical Cleansing Of Cow Udder Teatsmentioning

confidence: 99%

“…In contrast to [40,41] where all preparatory work is performed manually, the developed device provides maximum mechanization of the preparatory operations.…”

Section: Discussion Of the Results Obtained In Studying The Technical Means For Mechanical Cleansing Of Cow Udder Teatsmentioning

confidence: 99%

Development of a device for cleansing cow udder teats and testing it under industrial conditions

Paliy

Aliiev

Paliy

et al. 2021

EEJET

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Preparing cows for milking is one of the most important operations. Not only the speed of milk production but also the quality of milk depends on the level of the work performed. One of the most effective ways to mechanize the preparation of cows for milking implies the development of a special mechanical brush that cleans and stimulates the teat skin. As a result, there is no need to use additional foam detergents and napkins to cleanse and disinfect teats. A device has been designed for cleansing teats with two rotating brushes. Theoretical studies of the interaction of cleansing elements of the device for mechanical removal of pollutants from the udder teats in the course of milking operation were carried out. Assuming constancy of the modulus of elasticity, shape and roughness of teats, linear and angular velocities of brushes, nap stiffness, and homogeneity of physical and mechanical properties of contaminants on the teat, dependence of force Fe of the mechanical device on length l of the cleansing element and its speed ω was established. Under the condition that force Fe of the mechanical device is smaller than force Fp which causes pain but greater than the force retaining pollutants (adhesion), values of the main design and technological parameters of the developed device were determined: l=8 mm, ω=106 rpm. As a result of production tests, it was found that when using the developed device, the daily milk yield of the experimental group of cows exceeded that of the control group by an average of 1.1 times which has made it possible to obtain a supplement of 132.5 kg of milk. Along with this, there was a 0.19 % increase in milk fat content in the experimental group compared to the control group. The number of microorganisms decreased 2.2 times and the number of contaminant particles decreased 4.6 times

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“…However, Alex et al, (18) found that changes in milk production in response to extreme differences in milking frequency may be related to alterations in mitochondrial number and lactose synthesis, but not apoptosis. In addition, many scientists have dealt with the dynamics of udder emptying during milking (30)(31)(32)(33)(34)(35)(36)(37).…”

Section: Influence Of the Number Of Lactation On The Examined Milk Parametersmentioning

confidence: 99%

Optimization of Milk Performance and Quality in Dairy Farms by using a Quarter individual Milking System “MultiLactor”

Kaskous¹

2020

IJEAB

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Within the daily work on dairy farms milk harvesting is a crucial factor in optimizing milk performance and quality. The objective of this study was to investigate the influence of optimizing the milking process by using a quarter individual milking system "MultiLactor" (ML) on milk performance and quality. 170 Holstein-Friesian dairy cows were used on a farm in Switzerland. The cows were milked three times a day with an ML milking system. Recording of milk yield and collecting of milk sample were carried out monthly from each cow for one year. After that, the main milk parameters were analyzed by Association of Milk Records Switzerland. The primary milk data was processed with Excel program. Thereafter, analysis was carried out using the Statistical Analysis System (SAS). The average milk yield was 35.84±0.28 kg/cow/day. This corresponds to an annual milk yield of 12000 kg/cow. The mean values of milk composition were 4.36±0.02%, 3.28±0.01%, 4.75±0.01%, 20.65±0.20 mg/dl and 99.63±6.48 x 10 3 cells/ml for fat, protein, lactose, urea and somatic cell count (SCC) respectively. It was also shown that the cows remained healthy in the farm during the study period. In conclusion, the obtained results demonstrated that the cows produced a higher milk yield with good quality, since the milking system adapts the physiological requirements of dairy cows.

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A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level

Cited by 8 publications

References 78 publications

Method for short-term prediction of milk yield at the quarter level to improve udder health monitoring

Method for short-term prediction of milk yield at the quarter level to improve udder health monitoring

Development of a device for cleansing cow udder teats and testing it under industrial conditions

Optimization of Milk Performance and Quality in Dairy Farms by using a Quarter individual Milking System “MultiLactor”

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